Advances in computer processor speeds and other performance characteristics have occurred at a rapid pace in recent history, to the point that many human behaviors can now be thoroughly mimicked by machines. However, it has become apparent that current technology is insufficient for replication of certain activities. For example, the human brain tends to be quite adept at extracting data and drawing inferences and conclusions from complex sets of data. These inferences and conclusions may be used to describe the data in a way that allows another human to easily understand important events that occur in the data set. One such task that employs these reasoning faculties is the use of language to describe events in a concise, natural manner.
In an effort to enable computers and other machines to communicate data in a similar manner to human beings, example embodiments of the invention relate to Natural Language Generation (NLG) systems. These NLG systems function to parse data sets and to identify features within the dataset for communication to users, customers, other computer systems, or the like by expressing the features in a linguistic format. In some examples, a NLG system is configured to transform raw input data that is expressed in a non-linguistic format into a format that can be expressed linguistically, such as through the use of natural language. For example, raw input data may take the form of a value of a stock market index over time and, as such, the raw input data may include data that is suggestive of a time, a duration, a value and/or the like. Therefore, an NLG system may be configured to input the raw input data and output text that linguistically describes the value of the stock market index. For example, “securities markets rose steadily through most of the morning, before sliding downhill late in the day.”
Data that is input into a NLG system may be provided in, for example, a recurrent formal structure. The recurrent formal structure may comprise a plurality of individual fields and defined relationships between the plurality of individual fields. For example, the input data may be contained in a spreadsheet or database, presented in a tabulated log message or other defined structure, encoded in a ‘knowledge representation’ such as the resource description framework (RDF) triples that make up the Semantic Web and/or the like. In some examples, the data may include numerical content, symbolic content or the like. Symbolic content may include, but is not limited to, alphanumeric and other non-numeric character sequences in any character encoding, used to represent arbitrary elements of information. In some examples, the output of the NLG system is text in a natural language (e.g. English, Japanese or Swahili), but may also be in the form of synthesized speech.
In some examples, an NLG system may be configured to linguistically express a certain type of data. For example, the NLG system may be configured to describe sports statistics, financial data, weather data, or the like using terminology and linguistic expressions appropriate for the data set. Different terminology, phraseology, idioms, and the like may be used to describe different types of phenomena, and different data domains may require different analysis techniques for efficient generation of linguistic output. For example, an analysis operation for a set of sports data to generate a game recap may require different data analysis techniques than analysis of weather data to generate a weather forecast.
In some examples, input data may not be provided in a format that is readily usable for generation of natural language. In many cases, the NLG system may not be aware of how to extract relevant data from input sources that a human user can readily process. For example, it may be more straightforward for an NLG system to create a baseball game recap from a set of box score data than from a video replay of the game. In order to allow the NLG system to create the natural language recap, the data must be presented in a format that allows the NLG system to identify important relationships and relevant details among the data. One use case that presents such a challenge is a set of data related to object position over time. When presented with a set of raw image data describing the location of objects, current NLG systems are unable to detect relevant features of the location data that might be obvious to a human viewer.